The NIH BRAIN Initiative: Advancing neurotechnologies, integrating disciplines

Mott, Meghan; Gordon, Joshua A.; Koroshetz, Walter J.

doi:10.1371/journal.pbio.3000066

Cited by 47 publications

(32 citation statements)

References 6 publications

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“…Though unlikely, information might continue to grow linearly after an initial sublinear growth (Ecker et al, 2011). The only way to conclusively rule out this scenario is to record from all neurons in the information-encoding population, which, at least in mammals, will likely not be possible in the foreseeable future (Mott, Gordon, & Koroshetz, 2018).…”

Section: Discussionmentioning

confidence: 99%

Scaling of information in large neural populations reveals signatures of information-limiting correlations

Kafashan

Jaffe

Chettih

et al. 2020

Preprint

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How is information distributed across large neuronal populations within a given brain area? One possibility is that information is distributed roughly evenly across neurons, so that total information scales linearly with the number of recorded neurons. Alternatively, the neural code might be highly redundant, meaning that total information saturates. Here we investigated how information about the direction of a moving visual stimulus is distributed across hundreds of simultaneously recorded neurons in mouse primary visual cortex (V1). We found that information scales sublinearly, due to the presence of correlated noise in these populations. Using recent theoretical advances, we compartmentalized noise correlations into informationlimiting and nonlimiting components, and then extrapolated to predict how information grows when neural populations are even larger. We predict that tens of thousands of neurons are required to encode 95% of the information about visual stimulus direction, a number much smaller than the number of neurons in V1. Overall, these findings suggest that the brain uses a widely distributed, but nonetheless redundant code that supports recovering most information from smaller subpopulations. Figure 1. Information scaling in large neural populations, and the impact of noise correlations on information.a. The information that a population of neurons can encode about some stimulus value is always a non-decreasing function of the population size. Information might on average increase with every added neuron (unbounded scaling; red) if the information is evenly distributed across all neurons. In contrast, information can rapidly saturate if information is redundant, and thus it is not strictly limited by population size, but by other factors. In general, it has only been possible to record from a very small subset of neurons of a particular area (grey shaded), from which it is hard to tell the difference between the two scenarios if the sampled population size is too small. b. The encoded information is modulated by noise correlations. This is illustrated using two neurons with different tunings to the stimulus value (top). The amount of information to discriminate between two stimulus values ( " /red and # /blue) depends on the difference in mean population activity (crosses) between stimuli, and the noise correlations (shaded ellipsoids) for either stimulus (bottom, showing joint neural activity of both neurons). The information is largest when the noise is smallest in the direction of the mean population activity difference (black arrow), which leads to the largest separation across the optimal discrimination boundary (grey line). In this example, positive correlations boost information (middle), whereas negative correlations lower it (right), when compared to uncorrelated neurons (left). In general, the impact of noise correlations depends on how they interact with the population's tuning curves.

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Section: Discussionmentioning

confidence: 99%

Scaling of information in large neural populations reveals signatures of information-limiting correlations

Kafashan

Jaffe

Chettih

et al. 2020

Preprint

View full text Add to dashboard Cite

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“…More recently, brain-computer interface (BCI) or brain-machine interface (BMI) has been developed as motor neuroprosthetic device [6], [7], which could significantly promote the quality of life for those with serious motor impairments, such as spinal cord injury, stroke, and limb loss [8]. In research, such technology can help us picture how individual neurons and neural circuits work in the whole brain scale and discern how our brain processes, stores, and retrieves information [9]. All applications mentioned above require closed loop neural interface systems capable of reliably recording from the brain and delivering electrical stimulation to the targeted location.…”

Section: Introductionmentioning

confidence: 99%

Challenges in Scaling Down of Free-Floating Implantable Neural Interfaces to Millimeter Scale

Yang

2020

IEEE Access

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Implantable neural interface devices are an emerging technology for continuous brain monitoring and brain-computer interface (BCI). Recording of electrical signals from neurons of interest has led to more efficient and personalized diagnosis, treatment, and prognosis of neurological disorders. It facilitates the dynamic mapping of the whole brain, improving our understanding of the links between brain functions and behaviors. Stimulation of targeted nerves and neurons has been utilized as an effective therapy for Parkinson's disease, essential tremor, dystonia, etc. It has also been developed as motor neuroprosthetic devices, improving the quality of life of many. Although initial designs were bulky, recent advances in semiconductor and nano-, micro-technology has enabled the miniaturization of such devices to the millimeter scale. Free-floating neural implants of miniature size are highly scalable to be distributed around the targeted region of interest more extensively. They are more clinically viable as they cause less disturbance to the body and induce less tissue immune response. However, these research efforts for scaling down have faced challenges resulted from decreasing the size of such implants, including issues pertaining to wireless powering, data communication, neural signal recording, and neural stimulation. Through extensive literature research and simulations, the limits and trade-offs regarding neural implant miniaturization are investigated and analyzed for each aspect of the challenges. State-of-the-art development and future trends for advanced neural interfaces are also explored. INDEX TERMS Implantable neural interface, neural signal recording and stimulation, wireless power transfer, millimeter size, free-floating neural probes.

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“…Rapid advances in mapping single-cell transcriptional states have been made through the Human Cell Atlas (HCA) 1 and the NIH BRAIN initiatives, providing important insights into human health 2 . By considering transcript and protein compositions, functional categorization of cells is reshaping our models of ontology and helping discover new cell populations and elucidating heterogeneity.…”

Section: Introductionmentioning

confidence: 99%

A Protocol for Revealing Oral Neutrophil Heterogeneity by Single-Cell Immune Profiling in Human Saliva

Choudhury

Novotny

Aevermann

et al. 2020

Preprint

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Abstract Neutrophils are the most abundant white blood cells in the human body responsible for fighting viral, bacterial and fungi infections. Out of the 100 billion neutrophils produced daily, it is estimated that 10 % of these cells end up in oral biofluids. Because saliva is a fluid accessible through non-invasive techniques, it is an optimal source of cells and molecule surveillance in health and disease. While neutrophils are abundant in saliva, scientific advancements in neutrophil biology have been hampered likely due to their short life span, inability to divide once terminally differentiated, sensitivity to physical stress, and low RNA content. Here, we devise a protocol aiming to understand neutrophil heterogeneity by improving isolation methods, single-cell RNA extraction, sequencing and bioinformatic pipelines. Advanced flow cytometry 3D analysis, and machine learning validated our gating system model, by including positive neutrophil markers and excluding other immune cells and uncovered neutrophil heterogeneity. Considering specific cell markers, unique mitochondrial content, stringent and less stringent filtering strategies, our transcriptome single cell findings unraveled novel neutrophil subpopulations. Collectively, this methodology accelerates the discovery of salivary immune landscapes, with the promise of improving the understanding of diversification mechanisms, clinical diagnostics in health and disease, and guide targeted therapies.

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The NIH BRAIN Initiative: Advancing neurotechnologies, integrating disciplines

Cited by 47 publications

References 6 publications

Scaling of information in large neural populations reveals signatures of information-limiting correlations

Scaling of information in large neural populations reveals signatures of information-limiting correlations

Challenges in Scaling Down of Free-Floating Implantable Neural Interfaces to Millimeter Scale

A Protocol for Revealing Oral Neutrophil Heterogeneity by Single-Cell Immune Profiling in Human Saliva

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